'classification' 검색 결과

Build evaluation suites for AI features with the Axiom AI SDK. Generate test files, scoring functions, test cases, and configurable parameters from descriptions and existing code to compare changes and monitor output quality in production.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add regression evaluations for an AI feature
• Score classification, retrieval, and structured outputs
• Compare model and parameter configurations

Turn unstructured clinical encounter summaries into SOAP notes, a four-part format for medical records. Organize symptoms and findings, include diagnoses with ICD-10 classification codes, and document treatment steps, follow-up timing, and reasons to return for care.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Converting unstructured clinical summaries into SOAP notes
• Documenting diagnoses with ICD-10 codes and supporting reasoning
• Organizing treatment details, dosages, and timelines

Query EpiGraphDB for trait classifications, related literature, genetic evidence, and gene–drug relationships. Get concise summaries with limited result counts, or save the complete response when you need the underlying data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find ontology terms for a disease or trait
• Look up drugs associated with a gene
• Summarize literature and genetic evidence

Organize AI agent security findings using the ten OWASP ASI risk categories. Map issues such as leaked credentials or unauthorized tool calls to category IDs, and assign severity levels with suggested remediation timeframes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Classify AI agent security findings
• Assign categories to leaks and unauthorized tool calls
• Organize findings from multiple detection skills

Turn a product idea into a product requirements document through guided questions. Review existing system behavior and measurements as evidence, confirm how answers are recorded, and keep decisions separate from assumptions and unresolved items.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting product requirements for a new feature
• Reviewing existing behavior before deciding requirements
• Separating confirmed decisions from unresolved questions

Identify whether a Matrix chat room is a request source or a task workspace, then create or reuse a project-specific task room. The skill also handles worker invitation details and preserves information about where requests originated.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Identify the purpose of a Matrix room
• Prepare a task room for a new project
• Reuse an existing room for the same project

Review test files or suites for missing checks, hidden exceptions, unreliable timing, shared state, and maintenance problems. Get a severity-ranked report with specific locations, explanations, and concrete suggestions for improving the tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing whether existing tests meaningfully check results
• Investigating timing and execution-order risks in tests
• Prioritizing test improvements before editing code

Refactor C#/.NET code through renaming, extraction, consolidation, and other structural changes while preserving behavior. Check compatibility beyond code references, including public APIs and serialized names, then validate with builds and relevant tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Rename C# members and update their references
• Extract shared logic from duplicate code
• Reorganize code while preserving public API compatibility

Compare two or more dotnet new templates using their installed options, features, and supported frameworks. Get a focused comparison table and a recommendation that explains which template best fits the project you want to build.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing between webapi and webapp
• Comparing blazor and blazorwasm
• Selecting console or worker for background processing

Review C#/.NET code for about 50 inefficient API usage patterns across asynchronous work, memory, strings, collections, and I/O. Get exact occurrence counts, prioritized findings, and concrete fixes, with attention to frequently executed code.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing frequently executed C#/.NET code
• Finding unnecessary memory allocations
• Checking known performance pitfalls before release

Guide gh-aw release preparation and assess whether command-line changes break existing usage. The reference covers changesets, which record release changes, version previews, release steps, firewall log classification, and summaries of Go module usage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preview a release version and changelog
• Assess command-line compatibility and changeset types
• Interpret allowed and blocked traffic in firewall logs

Screen incoming nondisclosure agreements against a supplied playbook or default criteria. Receive a three-level classification, a summary of problematic clauses, suggested fixes, and recommended next steps for approval or legal review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Initial screening of NDAs received by sales
• Checking for non-compete clauses and missing exclusions
• Preparing an issue summary for legal review

Find test inputs and filter bypass examples for web security checks, organized by vulnerability type. Includes URL classification patterns, discovery wordlists, and examples of combining findings to demonstrate concrete security impact.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing test inputs for injection vulnerabilities
• Reviewing authentication and MFA bypass patterns
• Classifying URLs and selecting discovery wordlists

Write comprehensive literature reviews for medical imaging AI research. Use when writing survey papers, systematic reviews, or literature analyses on topics like segmentation, detection, classification in CT, MRI, X-ray, ultrasound, or pathology imaging. Triggers on requests for "review paper", "survey", "literature review", "综述", "systematic review", or mentions of writing academic reviews on deep learning for medical imaging.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This skill should be used when establishing comprehensive QA testing processes for any software project. Use when creating test strategies, writing test cases following Google Testing Standards, executing test plans, tracking bugs with P0-P4 classification, calculating quality metrics, or generating progress reports. Includes autonomous execution capability via master prompts and complete documentation templates for third-party QA team handoffs. Implements OWASP security testing and achieves 90% coverage targets.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Automatically checks system RAM/GPU before loading the model, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model. For classical statistical time series models (ARIMA, SARIMAX, VAR) use statsmodels; for time series classification/clustering use aeon.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

LQF Machine Learning Expert Guide - Routed skill for ML/Statistical Modeling with Critical Discussion Mode. Triggers on: machine learning, modeling, prediction, training, classification, regression, clustering, deep learning, neural network, model evaluation, feature engineering, hyperparameter tuning, overfitting, underfitting, baseline, ablation study, critique my approach, review my model, is this a good idea, should I use, what's wrong with, evaluate my solution, challenge my assumptions, discuss my approach Engages in critical discussion with minimum 3 rounds of iterative refinement. Challenges both user proposals and own suggestions with fact-based critique. Demands evidence and baselines before accepting solutions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Fine-tune public CodonFM Encodon checkpoints on labeled coding-sequence or coding-variant data using LoRA, head-only, or full fine-tuning. Use when a user explicitly asks to fine-tune CodonFM or Encodon for regression or classification. Support generic public-v1 Encodon workflows only; reject Decodon, MissenseDataset, missense_synom_agg, and generation workflows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with image-evidence-driven investigation. Use when analyzing ChangeNet model failures, investigating poor recall / FAR / PASS-NO_PASS metrics, auditing visual inspection pipeline quality, or running an RCA report for an AOI defect-detection model. Trigger phrases include "RCA on my ChangeNet model", "why is my AOI model failing", "audit ChangeNet predictions", "investigate FAR regressions", "root cause analysis on visual-changenet".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill when building, deploying, evaluating, debugging, or measuring latency for the DeepStream SOP Inference Microservice — a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary detection (GEBD) plus VLM classification. Trigger even if the user does not name it: verify operator step sequence, detect missing or out-of-order SOP steps, score factory/work-cell video for procedure compliance, run VLM-based SOP checking on industrial cameras, or call /v1/chat/completions with a file, RTSP, or Basler camera. Also trigger for its internals: SOPVideoProcessor, DeepStream GEBD model (e.g. DDM) via Triton CAPI, nvds_custom_postprocess, Cosmos Reason 1/2 vLLM, SSE streaming, Kafka NvProto/JSON output, Basler/Pylon camera + emulation, Docker compose, chunk-level latency. Do NOT trigger for generic DeepStream pipelines, object detection/tracking, NIM imports, or video summarization.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding "hard positive" benchmark controls for human genomic variants.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.